plot_accuracy_regular.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mlelarge/graph_neural_net/blob/main/plot_accuracy_regular.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# If running on Colab, uncomment the code below:\n",
        "#!git clone https://github.com/mlelarge/graph_neural_net.git\n",
        "#!pip install lightning\n",
        "#%cd graph_neural_net"
      ],
      "metadata": {
        "id": "7pu5pDuUHako",
        "outputId": "c6b3849d-ad8d-4da1-d219-c68c3f4da9aa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "id": "7pu5pDuUHako",
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/graph_neural_net\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "245b4687",
      "metadata": {
        "id": "245b4687"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from pathlib import Path\n",
        "import json\n",
        "import numpy as np\n",
        "import torch\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import pytorch_lightning as pl\n",
        "from models import get_siamese_model_test\n",
        "import loaders.data_generator as dg\n",
        "from loaders.loaders import siamese_loader\n",
        "from toolbox.utils import check_dir\n",
        "import loaders.data_generator as dg\n",
        "from loaders.loaders import siamese_loader\n",
        "\n",
        "ROOT_DIR = Path.home()\n",
        "path_dataset = os.path.join(ROOT_DIR, 'data/')\n",
        "\n",
        "from toolbox.metrics import all_losses_acc, accuracy_linear_assignment\n",
        "from toolbox.losses import triplet_loss\n",
        "\n",
        "criterion = triplet_loss()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2c7b53c3",
      "metadata": {
        "id": "2c7b53c3"
      },
      "source": [
        "# Downloading the pretrained model\n",
        "\n",
        "The cell below should only be run once, it creates a folder `downloads/` and then downloads in this folder the pretrained model and the configuration file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "d43f0179",
      "metadata": {
        "id": "d43f0179"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "config_url = 'https://github.com/mlelarge/graph_neural_net/releases/download/v1-qap/config.json'\n",
        "model_url = 'https://github.com/mlelarge/graph_neural_net/releases/download/v1-qap/epoch.98-step.7821.ckpt'\n",
        "cwd = os.getcwd()\n",
        "downloads = os.path.join(cwd, 'downloads')\n",
        "check_dir(downloads)\n",
        "\n",
        "r = requests.get(config_url)\n",
        "with open(cwd+'/downloads/config.json', 'wb') as f:\n",
        "    f.write(r.content)\n",
        "\n",
        "r = requests.get(model_url)\n",
        "with open(cwd+'/downloads/epoch.98-step.7821.ckpt', 'wb') as f:\n",
        "    f.write(r.content)\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "4a1497fa",
      "metadata": {
        "id": "4a1497fa"
      },
      "outputs": [],
      "source": [
        "name = cwd+'/downloads/epoch.98-step.7821.ckpt'\n",
        "path = cwd+'/downloads/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "fe9083af",
      "metadata": {
        "id": "fe9083af"
      },
      "outputs": [],
      "source": [
        "def get_device_config(path):\n",
        "    config_file = os.path.join(path,'config.json')\n",
        "    with open(config_file) as json_file:\n",
        "        config_model = json.load(json_file)\n",
        "    use_cuda = not config_model['cpu'] and torch.cuda.is_available()\n",
        "    device = 'cuda' if use_cuda else 'cpu'\n",
        "    return config_model, device"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "c95fc627",
      "metadata": {
        "id": "c95fc627"
      },
      "outputs": [],
      "source": [
        "config_model, device = get_device_config(path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "650423d8",
      "metadata": {
        "id": "650423d8",
        "outputId": "57edce5c-f30c-4a9c-9429-deeeb776f27f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.utilities.migration.utils:Lightning automatically upgraded your loaded checkpoint from v1.9.0 to v2.0.7. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint --file downloads/epoch.98-step.7821.ckpt`\n"
          ]
        }
      ],
      "source": [
        "model = get_siamese_model_test(name, config_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "ee2981c1",
      "metadata": {
        "id": "ee2981c1"
      },
      "outputs": [],
      "source": [
        "config_data = config_model['data']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "92709df1",
      "metadata": {
        "id": "92709df1"
      },
      "outputs": [],
      "source": [
        "num = 23\n",
        "list_noise = np.linspace(0, 0.22, num=num)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "964599b4",
      "metadata": {
        "id": "964599b4"
      },
      "outputs": [],
      "source": [
        "def compute_all(list_noise,config_data,path_dataset,model,device,bs=32):\n",
        "    all_acc = np.zeros((len(list_noise),config_data['test']['num_examples_test']))\n",
        "    for i,noise in enumerate(list_noise):\n",
        "        config_data['test']['noise']=noise\n",
        "        gene_test = dg.QAP_Generator('test', config_data['test'], path_dataset)\n",
        "        gene_test.load_dataset()\n",
        "        test_loader = siamese_loader(gene_test, bs, gene_test.constant_n_vertices, shuffle=False)\n",
        "        _, all_acc[i,:] = all_losses_acc(test_loader,model,criterion,device,eval_score=accuracy_linear_assignment)\n",
        "    return all_acc"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2edc01ce",
      "metadata": {
        "id": "2edc01ce"
      },
      "source": [
        "# Inference\n",
        "\n",
        "The cell below will create dataset of graphs with various level of noise if they do not exist, otherwise it will only read them."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "9c9bd8cb",
      "metadata": {
        "id": "9c9bd8cb",
        "outputId": "6dc9930a-6068-410f-cbe2-211c0222ac31",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.0_0.2/test.pkl\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  warnings.warn(_create_warning_msg(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.01_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.02_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.03_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.04_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.05_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.06_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.07_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.08_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.09_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.1_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.11_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.12_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.13_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.14_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.15_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.16_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.17_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.18_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.19_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.2_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.21_0.2/test.pkl\n",
            "Reading dataset at /root/data/QAP_Regular_ErdosRenyi_1000_50_1.0_0.22_0.2/test.pkl\n"
          ]
        }
      ],
      "source": [
        "all_a = compute_all(list_noise,config_data,path_dataset,model,device);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "id": "f8000455",
      "metadata": {
        "id": "f8000455"
      },
      "outputs": [],
      "source": [
        "def compute_quant(all_acc,quant_low=0.1,quant_up=0.9):\n",
        "    median_acc = np.median(all_acc,1)\n",
        "    num = len(median_acc)\n",
        "    q_acc = np.zeros((num,2))\n",
        "    for i in range(num):\n",
        "        q_acc[i,:] = np.quantile(all_acc[i,:],[quant_low, quant_up])\n",
        "    return median_acc, q_acc\n",
        "\n",
        "def acc_2_error(median_acc, q_acc):\n",
        "    error = q_acc-median_acc[:,np.newaxis]\n",
        "    error[:,0] = -error[:,0]\n",
        "    return error"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "343eac94",
      "metadata": {
        "id": "343eac94"
      },
      "source": [
        "# Results\n",
        "\n",
        "The FGNN has been trained with regular graphs with $50$ vertices, average degree $10$ and noise level $0.1$. The accuracy below is the fraction of matched vertices between two noisy versions of a given graph at various level of noise."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "e498d6e5",
      "metadata": {
        "id": "e498d6e5"
      },
      "outputs": [],
      "source": [
        "quant_low=0.1\n",
        "quant_up=0.9\n",
        "mc_50, q50 = compute_quant(all_a,quant_low=quant_low,quant_up=quant_up)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "1cebcdf9",
      "metadata": {
        "id": "1cebcdf9",
        "outputId": "88428e6c-9710-479c-a613-05c40223f72f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "error_50 = acc_2_error(mc_50,q50)\n",
        "\n",
        "plt.errorbar(list_noise,mc_50,yerr=error_50.T,label='FGNN (median)');\n",
        "plt.xlabel('noise (fraction of noisy edges)')\n",
        "plt.ylabel('accuracy')\n",
        "plt.title(f'Graphs with avg. degree 10 (quantiles {int(100*quant_low)}-{int(100*quant_up)}%)')\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "41802f3f",
      "metadata": {
        "id": "41802f3f"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "gnn12",
      "language": "python",
      "name": "gnn12"
    },
    "language_info": {
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